# Windows Local Deployment Guide ## 📖 Overview This document provides detailed instructions for deploying LightX2V locally on Windows environments, including batch file inference, Gradio Web interface inference, and other usage methods. ## 🚀 Quick Start ### Environment Requirements #### Hardware Requirements - **GPU**: NVIDIA GPU, recommended 8GB+ VRAM - **Memory**: Recommended 16GB+ RAM - **Storage**: Strongly recommended to use SSD solid-state drives, mechanical hard drives will cause slow model loading #### Software Requirements - **Operating System**: Windows 10/11 - **Python**: 3.12 or higher version - **CUDA**: 12.4 or higher version - **Dependencies**: Refer to LightX2V project's requirements_win.txt ### Installation Steps 1. **Clone Project** ```cmd git clone https://github.com/ModelTC/LightX2V.git cd LightX2V ``` 2. **Install Dependencies** ```cmd pip install -r requirements_win.txt ``` 3. **Download Models** Refer to [Model Download Guide](../getting_started/quickstart.md) to download required models ## 🎯 Usage Methods ### Method 1: Using Batch File Inference Refer to [Quick Start Guide](../getting_started/quickstart.md) to install environment, and use [batch files](https://github.com/ModelTC/LightX2V/tree/main/scripts/win) to run. ### Method 2: Using Gradio Web Interface Inference #### Manual Gradio Configuration Refer to [Quick Start Guide](../getting_started/quickstart.md) to install environment, refer to [Gradio Deployment Guide](./deploy_gradio.md) #### One-Click Gradio Startup (Recommended) **📦 Download Software Package** - [Baidu Cloud]() - To be added - [Quark Cloud]() - To be added **📁 Directory Structure** After extraction, ensure the directory structure is as follows: ``` ├── env/ # LightX2V environment directory ├── LightX2V/ # LightX2V project directory ├── start_lightx2v.bat # One-click startup script ├── lightx2v_config.txt # Configuration file ├── LightX2V使用说明.txt # LightX2V usage instructions └── models/ # Model storage directory ├── 说明.txt # Model documentation ├── Wan2.1-I2V-14B-480P-Lightx2v/ # Image-to-video model (480P) ├── Wan2.1-I2V-14B-720P-Lightx2v/ # Image-to-video model (720P) ├── Wan2.1-I2V-14B-480P-StepDistill-CfgDistil-Lightx2v/ # Image-to-video model (4-step distillation, 480P) ├── Wan2.1-I2V-14B-720P-StepDistill-CfgDistil-Lightx2v/ # Image-to-video model (4-step distillation, 720P) ├── Wan2.1-T2V-1.3B-Lightx2v/ # Text-to-video model (1.3B parameters) ├── Wan2.1-T2V-14B-Lightx2v/ # Text-to-video model (14B parameters) └── Wan2.1-T2V-14B-StepDistill-CfgDistill-Lightx2v/ # Text-to-video model (4-step distillation) ``` **📋 Configuration Parameters** Edit the `lightx2v_config.txt` file and modify the following parameters as needed: ```ini # Task type (i2v: image-to-video, t2v: text-to-video) task=i2v # Interface language (zh: Chinese, en: English) lang=en # Server port port=8032 # GPU device ID (0, 1, 2...) gpu=0 # Model size (14b: 14B parameter model, 1.3b: 1.3B parameter model) model_size=14b # Model class (wan2.1: standard model, wan2.1_distill: distilled model) model_cls=wan2.1 ``` **⚠️ Important Note**: If using distilled models (model names containing StepDistill-CfgDistil field), please set `model_cls` to `wan2.1_distill` **🚀 Start Service** Double-click to run the `start_lightx2v.bat` file, the script will: 1. Automatically read configuration file 2. Verify model paths and file integrity 3. Start Gradio Web interface 4. Automatically open browser to access service **💡 Usage Suggestion**: After opening the Gradio Web page, it's recommended to check "Auto-configure Inference Options", the system will automatically select appropriate optimization configurations for your machine. When reselecting resolution, you also need to re-check "Auto-configure Inference Options". **⚠️ Important Note**: On first run, the system will automatically extract the environment file `env.zip`, which may take several minutes. Please be patient. Subsequent launches will skip this step. You can also manually extract the `env.zip` file to the current directory to save time on first startup. ### Method 3: Using ComfyUI Inference TODO - To be added ComfyUI integration guide